The DNN model contains seven dense layers with a ReLu activation function and a softmax output layer. Model is trained to minimize categorical cross-entropy loss [1]}. Adam algorithm is used to optimize the model. A Drop-out layer of 0.3, i.e. 30 %, is used before three dense layers to avoid overfitting. A batch size of 64 is taken, and the model got trained for 50 epochs. The confusion matrix of classification is shown in figure 11, and the classification report is shown in Table 2.
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